Load packages

Load Hardship-list

Scale and reverse coding

[1] "code"               "label"              "codeWVS"            "homiciderate"       "gdp"                "infantmortality"   
[7] "lifeexpectancy"     "gini"               "femalemale_primedu"

Data of Wave 5

#rename the variables

  [1] "wave"          "V1A"           "V1B"           "country_code"  "V2A"           "V3"            "V4"           
  [8] "V4_CO"         "V5"            "V5_CO"         "V6"            "V6_CO"         "V7"            "V7_CO"        
 [15] "V8"            "V8_CO"         "V9"            "V9_CO"         "V10"           "V11"           "V12"          
 [22] "V13"           "V14"           "V15"           "V16"           "V17"           "V18"           "V19"          
 [29] "V20"           "V21"           "V22"           "V23"           "V24"           "V25"           "V26"          
 [36] "V27"           "V28"           "V29"           "V30"           "V31"           "V32"           "V33"          
 [43] "V34"           "V35"           "V36"           "V37"           "V38"           "V39"           "V40"          
 [50] "V41"           "V42"           "V43"           "V43_01"        "V43_02"        "V43_03"        "V43_04"       
 [57] "V43_05"        "V43_06"        "V43_07"        "V43_08"        "V43_09"        "V43_10"        "V43_11"       
 [64] "V43_12"        "V43_13"        "V43_14"        "V43_15"        "V43_16"        "V43_17"        "V43_18"       
 [71] "V43_19"        "V43_20"        "V43_21"        "V43_22"        "V43_23"        "V43_24"        "V43_25"       
 [78] "V43_26"        "V43_27"        "V43_28"        "V43_29"        "V43_30"        "V44"           "V45"          
 [85] "V46"           "V47"           "V48"           "V49"           "V50"           "V51"           "V52"          
 [92] "V53"           "V54"           "married"       "children"      "V57"           "V58"           "V59"          
 [99] "V60"           "V61"           "V62"           "V63"           "V64"           "V65"           "V66"          
[106] "V67"           "V68"           "V69"           "V69_HK"        "V70"           "V70_HK"        "V71"          
[113] "V72"           "V73"           "V73_HK"        "V74"           "V74_HK"        "V75"           "V76"          
[120] "V77"           "V78"           "V79"           "V80"           "V81"           "V82"           "V83"          
[127] "V84"           "V85"           "risktaking"    "V87"           "V88"           "V89"           "V90"          
[134] "V91"           "V92"           "V93"           "V94"           "V95"           "V96"           "V97"          
[141] "V98"           "V99"           "V100"          "V101"          "V102"          "V103"          "V104"         
[148] "V105"          "V106"          "V107"          "V108"          "V109"          "V110"          "V111"         
[155] "V112"          "V113"          "V114"          "V115"          "V116"          "V117"          "V118"         
[162] "V119"          "V120"          "V121"          "V122"          "V123"          "V124"          "V125"         
[169] "V126"          "V127"          "V128"          "V129"          "V130"          "V130_CA_1"     "V130_IQ_1"    
[176] "V130_IQ_2"     "V130_IQ_3"     "V130_IQ_4"     "V130_NZ_1"     "V130_NZ_2"     "V131"          "V132"         
[183] "V133"          "V134"          "V135"          "V136"          "V137"          "V138"          "V139"         
[190] "V140"          "V141"          "V142"          "V143"          "V144"          "V145"          "V146_00"      
[197] "V146_01"       "V146_02"       "V146_03"       "V146_04"       "V146_05"       "V146_06"       "V146_07"      
[204] "V146_08"       "V146_09"       "V146_10"       "V146_11"       "V146_12"       "V146_13"       "V146_14"      
[211] "V146_15"       "V146_16"       "V146_17"       "V146_18"       "V146_19"       "V146_20"       "V146_21"      
[218] "V146_22"       "V147"          "V148"          "V149"          "V150"          "V151"          "V151_IQ_A"    
[225] "V151_IQ_B"     "V152"          "V153"          "V154"          "V155"          "V156"          "V157"         
[232] "V158"          "V159"          "V160"          "V161"          "V162"          "V163"          "V164"         
[239] "V165"          "V166"          "V167"          "V168"          "V169"          "V170"          "V171"         
[246] "V172"          "V173"          "V174"          "V175"          "V176"          "V177"          "V178"         
[253] "V179"          "V180"          "V181"          "V182"          "V183"          "V184"          "V185"         
[260] "V186"          "V187"          "V188"          "V189"          "V190"          "V191"          "V192"         
[267] "V193"          "V194"          "V195"          "V196"          "V197"          "V198"          "V199"         
[274] "V200"          "V201"          "V202"          "V203"          "V204"          "V205"          "V206"         
[281] "V207"          "V208"          "V209"          "V210"          "V211"          "V212"          "V213A"        
[288] "V213B"         "V213C"         "V213D"         "V213E"         "V213F"         "V213G"         "V213H"        
[295] "V213K"         "V213L"         "V213M"         "V213N"         "V214"          "V215"          "V216"         
[302] "V217"          "V218"          "V219"          "V220"          "V221"          "V222"          "V223"         
[309] "V224"          "V225"          "V226"          "V227"          "V228"          "V229"          "V230"         
[316] "V231"          "V232"          "V233"          "V233A"         "V234"          "gender"        "V236"         
[323] "age"           "education"     "V238CS"        "V239"          "V240"          "employed"      "V242"         
[330] "V242A_CO"      "V243"          "V244"          "V245"          "V246"          "V247"          "V248"         
[337] "V249"          "V250"          "V251"          "V252"          "V252B"         "V253"          "V253CS"       
[344] "V254"          "V255"          "V255CS"        "V256"          "V257"          "V257B"         "V257C"        
[351] "V258"          "V259"          "V259A"         "V260"          "V261"          "V262"          "V263"         
[358] "V264"          "V265"          "S024"          "S025"          "Y001"          "Y002"          "Y003"         
[365] "SACSECVAL"     "SECVALWGT"     "RESEMAVAL"     "WEIGHTB"       "I_AUTHORITY"   "I_NATIONALISM" "I_DEVOUT"     
[372] "DEFIANCE"      "WEIGHT1A"      "I_RELIGIMP"    "I_RELIGBEL"    "I_RELIGPRAC"   "DISBELIEF"     "WEIGHT2A"     
[379] "I_NORM1"       "I_NORM2"       "I_NORM3"       "RELATIVISM"    "WEIGHT3A"      "I_TRUSTARMY"   "I_TRUSTPOLICE"
[386] "I_TRUSTCOURTS" "SCEPTICISM"    "WEIGHT4A"      "I_INDEP"       "I_IMAGIN"      "I_NONOBED"     "AUTONOMY"     
[393] "WEIGHT1B"      "I_WOMJOB"      "I_WOMPOL"      "I_WOMEDU"      "EQUALITY"      "WEIGHT2B"      "I_HOMOLIB"    
[400] "I_ABORTLIB"    "I_DIVORLIB"    "CHOICE"        "WEIGHT3B"      "I_VOICE1"      "I_VOICE2"      "I_VOI2_00"    
[407] "VOICE"         "WEIGHT4B"      "S001"          "S007"          "S018"          "S019"          "S021"         
[414] "COW"          

Read countrynames data from the CSV file (to decode the dataset 5)


              Andorra             Argentina             Australia                Brazil              Bulgaria 
                 1003                  1002                  1421                  1500                  1001 
         Burkina Faso                Canada                 Chile                 China              Colombia 
                 1534                  2164                  1000                  1991                  3025 
           Cyprus (G)                 Egypt              Ethiopia               Finland                France 
                 1050                  3051                  1500                  1014                  1001 
              Georgia               Germany                 Ghana         Great Britain             Guatemala 
                 1500                  2064                  1534                  1041                  1000 
            Hong Kong               Hungary                 India             Indonesia                  Iran 
                 1252                  1007                  2001                  2015                  2667 
                 Iraq                 Italy                 Japan                Jordan              Malaysia 
                 2701                  1012                  1096                  1200                  1201 
                 Mali                Mexico               Moldova               Morocco           Netherlands 
                 1534                  1560                  1046                  1200                  1050 
          New Zealand                Norway                  Peru                Poland               Romania 
                  954                  1025                  1500                  1000                  1776 
               Russia                Rwanda Serbia and Montenegro              Slovenia          South Africa 
                 2033                  1507                  1220                  1037                  2988 
          South Korea                 Spain                Sweden           Switzerland                Taiwan 
                 1200                  1200                  1003                  1241                  1227 
             Thailand   Trinidad and Tobago                Turkey               Ukraine         United States 
                 1534                  1002                  1346                  1000                  1249 
              Uruguay              Viet Nam                Zambia 
                 1000                  1495                  1500 

#Read Dataset (Wave 6)

#rename the variables

Add the countrynames


            Algeria           Argentina             Armenia           Australia          Azerbaijan             Belarus 
               1200                1030                1100                1477                1002                1535 
             Brazil               Chile               China            Colombia          Cyprus (G)             Ecuador 
               1486                1000                2300                1512                1000                1202 
              Egypt             Estonia             Georgia             Germany               Ghana               Haiti 
               1523                1533                1202                2046                1552                1996 
          Hong Kong               India                Iraq               Japan              Jordan          Kazakhstan 
               1000                4078                1200                2443                1200                1500 
             Kuwait          Kyrgyzstan             Lebanon               Libya            Malaysia              Mexico 
               1303                1500                1200                2131                1300                2000 
            Morocco         Netherlands         New Zealand             Nigeria            Pakistan           Palestine 
               1200                1902                 841                1759                1200                1000 
               Peru         Philippines              Poland               Qatar             Romania              Russia 
               1210                1200                 966                1060                1503                2500 
             Rwanda           Singapore            Slovenia        South Africa         South Korea               Spain 
               1527                1972                1069                3531                1200                1189 
             Sweden              Taiwan            Thailand Trinidad and Tobago             Tunisia              Turkey 
               1206                1238                1200                 999                1205                1605 
            Ukraine       United States             Uruguay          Uzbekistan               Yemen            Zimbabwe 
               1500                2232                1000                1500                1000                1500 

Bind data

 [1]  38  24  48  28  54  44  70  60  27  35  73  33  46  41  59  26  45  21  22  31  20  56  29  30  61  47  40  57  67  43  25
[32]  62  23  64  42  19  52  51  39  36  37  49  34  71  32  69  50  63  65  55  72  18  58  68  75  53  78  76  88  66  84  79
[63]  77  74  80  82  83  85  86  90  81  87  -2  89  94  95  17  16  91  92  -1  93  15  -5  97  98  NA  99 102
[1] NA NA

Remove missing data

Transfrom risk item such that high values represent more risk taking

Dichotom variables

 [1] "gender"             "age"                "country_code"       "wave"               "risktaking"         "children"          
 [7] "married"            "employed"           "education"          "country"            "T_score_risktaking" "Z_score_risktaking"
[13] "z_score_age"       
[1] "label"          "code"           "hardship_index"

Add hardship to WVS_mixed_model

Mixed Models

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Z_score_risktaking ~ 1 + (1 | country)
   Data: WVS_mixed_model

REML criterion at convergence: 409702.8

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.31854 -0.82565 -0.07222  0.76171  2.53255 

Random effects:
 Groups   Name        Variance Std.Dev.
 country  (Intercept) 0.08536  0.2922  
 Residual             0.90271  0.9501  
Number of obs: 149626, groups:  country, 77

Fixed effects:
            Estimate Std. Error       df t value Pr(>|t|)
(Intercept)  0.01170    0.03341 76.10694    0.35    0.727

age, sex

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: risktaking ~ 1 + scale(age) + factor(gender) + (1 + scale(age) +      factor(gender) | country)
   Data: WVS_data
Control: lmerControl(optimizer = "bobyqa")

REML criterion at convergence: 539930.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4750 -0.7820 -0.0772  0.7523  3.2237 

Random effects:
 Groups   Name            Variance Std.Dev. Corr     
 country  (Intercept)     0.16689  0.4085            
          scale(age)      0.01956  0.1399   0.31     
          factor(gender)1 0.02207  0.1486   0.12 0.25
 Residual                 2.15081  1.4666            
Number of obs: 149626, groups:  country, 77

Fixed effects:
                Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)      3.42058    0.04697 76.22127   72.83   <2e-16 ***
scale(age)      -0.32274    0.01659 74.09811  -19.46   <2e-16 ***
factor(gender)1 -0.36831    0.01887 73.38849  -19.52   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g)
scale(age)  0.296        
fctr(gndr)1 0.067  0.222 

#model 2

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: risktaking ~ 1 + scale(age) + factor(gender) + factor(children) +  
    factor(married) + factor(employed) + factor(education) +  
    (1 + scale(age) + factor(gender) + factor(children) + factor(married) +  
        factor(employed) + factor(education) | country)
   Data: WVS_data
Control: lmerControl(optCtrl = list(maxfun = 30000), optimizer = "bobyqa")

REML criterion at convergence: 538463.9

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.56649 -0.78106 -0.08451  0.74253  3.15001 

Random effects:
 Groups   Name               Variance Std.Dev. Corr                               
 country  (Intercept)        0.139167 0.37305                                     
          scale(age)         0.013141 0.11464   0.23                              
          factor(gender)1    0.023396 0.15296   0.00  0.25                        
          factor(children)1  0.021111 0.14529   0.09  0.17  0.08                  
          factor(married)1   0.010402 0.10199   0.18  0.45  0.54  0.25            
          factor(employed)1  0.007348 0.08572   0.01  0.06  0.03 -0.29 -0.23      
          factor(education)1 0.015082 0.12281  -0.17  0.07  0.10 -0.15  0.08  0.19
 Residual                    2.125987 1.45808                                     
Number of obs: 149626, groups:  country, 77

Fixed effects:
                   Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)         3.52589    0.04497 77.43914  78.411  < 2e-16 ***
scale(age)         -0.23515    0.01418 72.29489 -16.584  < 2e-16 ***
factor(gender)1    -0.34507    0.01948 73.75931 -17.717  < 2e-16 ***
factor(children)1  -0.20808    0.02069 72.08925 -10.056 2.30e-15 ***
factor(married)1   -0.13608    0.01562 62.63906  -8.712 2.14e-12 ***
factor(employed)1   0.01736    0.01331 68.34140   1.304    0.197    
factor(education)1  0.12489    0.01869 51.42871   6.683 1.66e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) fctr(g)1 fctr(c)1 fctr(mr)1 fctr(mp)1
scale(age)   0.202                                             
fctr(gndr)1 -0.048  0.223                                      
fctr(chld)1  0.001  0.029  0.015                               
fctr(mrrd)1  0.104  0.308  0.386   -0.049                      
fctr(mply)1 -0.055  0.083  0.088   -0.208   -0.154             
fctr(dctn)1 -0.263  0.106  0.074   -0.081    0.036     0.065   

Model3

Error in model.frame.default(data = WVS_data, drop.unused.levels = TRUE,  : 
  invalid type (list) for variable 'hardship'

ANOVA

---
title: "R Notebook"
output: html_notebook
---


# Load packages
```{r}
library(data.table)
library(tidyr)
library(maps)
library(haven)
library(ggplot2)
library(dplyr)
library(readxl)
library(lmerTest)
library(lme4)
```

# Load Hardship-list
```{r}
hardship_complete <- read_excel("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/Hardship/Hardship_complete_2024.xlsx")
hardship_complete
```

# Scale and reverse coding
```{r}
# Überprüfen der Spaltennamen
colnames(hardship_complete)

# Entfernen von Leerzeichen in den Spaltennamen
colnames(hardship_complete) <- make.names(colnames(hardship_complete))

# Versuche erneut, die Daten zu bearbeiten
hardship_complete$homiciderate <- log(hardship_complete$homiciderate)
hardship_complete$gdp <- log(hardship_complete$gdp)
hardship_complete$infantmortality <- log(hardship_complete$infantmortality)
hardship_complete$lifeexpectancy <- log(hardship_complete$lifeexpectancy)
hardship_complete$gini <- log(hardship_complete$gini)

# Reverse Codierung
hardship_complete$homiciderate <- scale(hardship_complete$homiciderate)
hardship_complete$gdp <- scale(-hardship_complete$gdp)
hardship_complete$infantmortality <- scale(hardship_complete$infantmortality)
hardship_complete$lifeexpectancy <- scale(-hardship_complete$lifeexpectancy)
hardship_complete$gini <- scale(hardship_complete$gini)

# Neuberechnung des Hardship-Index
hardship_complete$hardship_index <- (hardship_complete$homiciderate + hardship_complete$gdp +
                                      hardship_complete$gini + hardship_complete$lifeexpectancy +
                                      hardship_complete$infantmortality + hardship_complete$femalemale_primedu) / 6

hardship_complete
```

# Data of Wave 5 
```{r}
WV5_data <- readRDS("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/F00007944-WV5_Data_R_v20180912.rds")

# Convert WV5_data-object in data.frame 
WV5_data_df <- as.data.frame(WV5_data)

# show first five columns
WV5_data_df
```

#rename the variables
```{r}
WV5_data <- WV5_data_df %>%
  rename(gender = V235, age = V237, country_code = V2, wave = V1, risktaking = V86, children = V56, married = V55, employed = V241, education = V238)
WV5_data

colnames(WV5_data)

#select only the variables of interest
WV5_data <- WV5_data %>%
  dplyr::select(gender, age, country_code, wave, risktaking, children, married, employed, education)
WV5_data
```

# Read countrynames data from the CSV file (to decode the dataset 5)
```{r}
countrynames <- read.csv("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/countrynames.txt", header = FALSE, as.is = TRUE)
colnames(countrynames) <- c("code", "name")

# Assuming WV5_data has a column named country_code
WV5_data$country <- countrynames$name[match(WV5_data$country_code, countrynames$code)]

# Check the frequency of each country in the new column
table(WV5_data$country)

# Display the updated WV5_data
print(WV5_data)
```

#Read Dataset (Wave 6)
```{r}
WV6_data <- load("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 
print(WV6_data)
```


#rename the variables
```{r}
WV6_data <- WV6_data %>%
  rename(wave = V1, gender = V240, age = V242,country_code = V2, risktaking = V76, children = V58, married = V57, employed = V229, education = V248)

#select only the variables of interest
WV6_data <- WV6_data %>%
  dplyr::select(wave, gender, age, country_code,risktaking, children, married, employed, education)
WV6_data
```

# Add the countrynames
```{r}
countrynames = read.csv("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV6_data$country = countrynames$name [match(WV6_data$country_code, countrynames$code)]
table(WV6_data$country)
WV6_data
```

# Bind data
```{r}
WV5_data
WV6_data
WVS_data = rbind(WV5_data, WV6_data)
WVS_data

unique(WVS_data$age)
range(WVS_data$age)
```

# Remove missing data
```{r}
WVS_data = subset(WVS_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)

WVS_data <- na.omit(WVS_data)

# Use the mutate function to change the country name
WVS_data <- WVS_data %>%
  mutate(country = ifelse(country == "Great Britain", "United Kingdom", country))
```

# Transfrom risk item such that high values represent more risk taking
```{r}
WVS_data$risktaking = 6 - WVS_data$risktaking + 1

# Transform risk variable into T-score (mean = 50, sd = 10)
WVS_data$T_score_risktaking = 10*scale(WVS_data$risktaking, center=TRUE,scale=TRUE)+50

WVS_data

#Transform risk variable into Z score 

# Assuming T-scores have a mean of 50 and a standard deviation of 10
WVS_data$Z_score_risktaking = (WVS_data$T_score_risktaking - 50) / 10

# Print the resulting data frame
print(WVS_data)

WVS_data <- WVS_data %>%
  group_by(country) %>%
  mutate(z_score_age = scale(age))
WVS_data
```
  
# Dichotom variables
```{r}
WVS_data$gender = ifelse(WVS_data$gender == 1, 0, 1) # sex: male vs. female
WVS_data$children = ifelse(WVS_data$children == 0, 0, 1) # children: no vs. yes
WVS_data$married = ifelse(WVS_data$married == 1, 1, 0) # married: yes vs. no
WVS_data$employed = ifelse(WVS_data$employed < 4, 1, 0) # employed: yes vs. no
WVS_data$education = ifelse(WVS_data$education < 4, 0, 1) # education: no primary vs. primary+ 


hardship <- hardship_complete %>%
  dplyr::select(label, code, hardship_index)
hardship
```
```{r}
colnames(WVS_data)
colnames(hardship)
```

# Add hardship to WVS_mixed_model
```{r}
WVS_mixed_model <- left_join(WVS_data, hardship, by = c("country" = "code"))
WVS_mixed_model
head(WVS_mixed_model)
```

# Mixed Models
```{r}
# intercept only model
model0 = lmer(Z_score_risktaking ~ 1 + (1|country),data = WVS_mixed_model)
summary_model0=summary(model0)
summary_model0
```
```{r}
head(WVS_mixed_model)
```


# age, sex 
```{r}
model1 <- lmer(risktaking ~ 1 + scale(age) + factor(gender) + (1 + scale(age) + factor(gender) | country), 
               data = WVS_data, 
                      control = lmerControl(optimizer = "bobyqa"))
summary_model1=summary(model1)
print(summary_model1) # Koeffizientenübersicht des Modells anzeigen
```

#model 2
```{r}
model2 = lmer(risktaking ~ 1+scale(age)+factor(gender) + factor(children) + factor(married) + factor(employed) + factor(education)+ (1+scale(age)+factor(gender)+ factor(children) + factor(married) + factor(employed) + factor(education)|country),data = WVS_data,control=lmerControl(optCtrl=list(maxfun=30000),optimizer="bobyqa"))
summary_model2=summary(model2)

print(summary_model2) 
```

# Model3
```{r}
model3 <- lmer(risktaking ~ 1+scale(age)*hardship+factor(gender)*hardship + factor(children) + factor(married) + factor(employed) + factor(education)+ (1+scale(age)+factor(gender)+ factor(children) + factor(married) + factor(employed) + factor(education)|country),data = WVS_data,control=lmerControl(optCtrl=list(maxfun=30000),optimizer="bobyqa"),REML = FALSE)
summary_model3=summary(model3)

print(summary_model3)
```

# ANOVA
```{r}
anova(model0,model1)
anova(model1,model_2)
anova(model_2,model_3) 
```

```{r}
coefsallmodels=rbind(summary_model1$coefficients,
summary_model_2$coefficients,
summary_model_3$coefficients[c(1:2,4:8,3,9:10),])

write.csv(coefsallmodels,"coefsallmodels.csv")
```


```{r}
# Read the CSV file into a data frame
gbd_mentalhealth <- read_excel("/Users/cristinacandido/Documents/Github/risk_wvs/GBD_mentalhealth.xlsx")

gbd_mentalhealth

#select only the variables of interest
gbd_mentalhealth <- gbd_mentalhealth %>%
  dplyr::select(country, gender, age, cause, val, Measure)
gbd_mentalhealth


library(dplyr)

# Group data by country and age group, and calculate summary statistics
summary_by_country_age <- gbd_mentalhealth %>%
  group_by(country, age) %>%
  summarise(
    mean_DALYs = mean(val))  # Calculate mean of DALYs

library(dplyr)

# Assuming 'summary_by_country_age' contains your summarized dataset
mean_by_country <- summary_by_country_age %>%
  group_by(country) %>%
  summarise(mean_DALYs = mean(mean_DALYs))

# View the resulting mean by country
print(mean_by_country)

#log transform
mean_by_country$mean_DALYs=log(mean_by_country$mean_DALYs)
 
mean_by_country 

#Reverse codierung 
mean_by_country$mean_DALYs=scale(mean_by_country$mean_DALYs)

mean_by_country

#rename mean_DALYS
mental_health_index <- mean_by_country %>%
  rename('mental_health' = mean_DALYs)
mental_health_index

```
```{r}
library(dplyr)

#######Anxiety disorders#########
#Filter data for a specific mental disorder (e.g., Anxiety Disorders)
anxiety_disorders <- gbd_mentalhealth %>%
  filter(cause == "Anxiety disorders")  # Change "Anxiety Disorders" to the desired disorder

# Calculate the mean of 'val' (Disability-Adjusted Life Years) across locations and ages
mean_DALYs <- mean(anxiety_disorders$val)

# Optionally, if you want to calculate mean by country:
anxiety_disorders <- anxiety_disorders %>%
  group_by(country) %>%
  summarise(mean_Anxiety_disorders = mean(val))

anxiety_disorders

#log transform
anxiety_disorders$mean_Anxiety_disorders=log(anxiety_disorders$mean_Anxiety_disorders)
 
anxiety_disorders

#Reverse codierung 
anxiety_disorders$mean_Anxiety_disorders=scale(anxiety_disorders$mean_Anxiety_disorders)
anxiety_disorders
mental_health_index

```
```{r}
gbd_mentalhealth

# Assuming gbd_mentalhealth is your data frame containing the 'cause' column

# Get unique values of 'cause' column
unique_causes <- unique(gbd_mentalhealth$cause)

# Create a data frame with unique causes
cause_table <- data.frame(Cause = unique_causes)

# Print the cause table
print(cause_table)


```
```{r}
#######Bulimia nervosa########
library(dplyr)

# Filter the data for 'Bulimia Nervosa'
bulimia_nervosa <- gbd_mentalhealth %>%
  filter(cause == "Bulimia nervosa")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(bulimia_nervosa$val)

# Optionally, calculate mean by country
bulimia_nervosa <- bulimia_nervosa %>%
  group_by(country) %>%
  summarise(mean_bulimia_nervosa = mean(val))

# Log transform the mean_bulimia_nervosa column
bulimia_nervosa$mean_bulimia_nervosa <- log(bulimia_nervosa$mean_bulimia_nervosa)

# Scale the mean_bulimia_nervosa column (if needed)
bulimia_nervosa$mean_bulimia_nervosa <- scale(bulimia_nervosa$mean_bulimia_nervosa)

# Print the resulting data frame
print(bulimia_nervosa)
print(anxiety_disorders)

gbd_mentalhealth

```
```{r}
#####Attention-deficit/hyperactivity disorder
library(dplyr)


ADHD <- gbd_mentalhealth %>%
  filter(cause == "Attention-deficit/hyperactivity disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(ADHD$val)

# Optionally, calculate mean by country
ADHD <- ADHD %>%
  group_by(country) %>%
  summarise(mean_ADHD = mean(val))

# Log transform the mean_bulimia_nervosa column
ADHD$mean_ADHD <- log(ADHD$mean_ADHD)

# Scale the mean_bulimia_nervosa column (if needed)
ADHD$mean_ADHD <- scale(ADHD$mean_ADHD)

# Print the resulting data frame
print(ADHD)

```
```{r}
#########Idiopathic development intellectual ability 
library(dplyr)


Idiopathic_developmental_intellectual_disability <- gbd_mentalhealth %>%
  filter(cause == "Idiopathic developmental intellectual disability")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(Idiopathic_developmental_intellectual_disability$val)

# Optionally, calculate mean by country
Idiopathic_developmental_intellectual_disability <- Idiopathic_developmental_intellectual_disability %>%
  group_by(country) %>%
  summarise(mean_Idiopathic_developmental_intellectual_disability = mean(val))

# Log transform the mean_bulimia_nervosa column
Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability <- log(Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability)

# Scale the mean_bulimia_nervosa column (if needed)
Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability <- scale(Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability)

# Print the resulting data frame
print(Idiopathic_developmental_intellectual_disability)
```
```{r}
######Anorexia Nervosa

anorexia_nervosa <- gbd_mentalhealth %>%
  filter(cause == "Anorexia nervosa")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(anorexia_nervosa$val)

# Optionally, calculate mean by country
anorexia_nervosa <- anorexia_nervosa %>%
  group_by(country) %>%
  summarise(mean_anorexia_nervosa = mean(val))

# Log transform the mean_bulimia_nervosa column
anorexia_nervosa$mean_anorexia_nervosa <- log(anorexia_nervosa$mean_anorexia_nervosa)

# Scale the mean_bulimia_nervosa column (if needed)
anorexia_nervosa$mean_anorexia_nervosa <- scale(anorexia_nervosa$mean_anorexia_nervosa)

# Print the resulting data frame
print(anorexia_nervosa)
```
```{r}
#####Depressive disorders#######

depressive_disorders <- gbd_mentalhealth %>%
  filter(cause == "Depressive disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(depressive_disorders$val)

# Optionally, calculate mean by country
depressive_disorders <- depressive_disorders %>%
  group_by(country) %>%
  summarise(mean_depressive_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
depressive_disorders$mean_depressive_disorders <- log(depressive_disorders$mean_depressive_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
depressive_disorders$mean_depressive_disorders <- scale(depressive_disorders$mean_depressive_disorders)

# Print the resulting data frame
print(depressive_disorders)
```

```{r}
#######Autismus spectrum disorders######

autismus_spectrum_disorders <- gbd_mentalhealth %>%
  filter(cause == "Autism spectrum disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(autismus_spectrum_disorders$val)

# Optionally, calculate mean by country
autismus_spectrum_disorders <- autismus_spectrum_disorders %>%
  group_by(country) %>%
  summarise(mean_autismus_spectrum_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
autismus_spectrum_disorders$mean_autismus_spectrum_disorders <- log(autismus_spectrum_disorders$mean_autismus_spectrum_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
autismus_spectrum_disorders$mean_autismus_spectrum_disorders <- scale(autismus_spectrum_disorders$mean_autismus_spectrum_disorders)

# Print the resulting data frame
print(autismus_spectrum_disorders)
```

```{r}
######Schizophrenia#######
schizophrenia <- gbd_mentalhealth %>%
  filter(cause == "Schizophrenia")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(schizophrenia$val)

# Optionally, calculate mean by country
schizophrenia <- schizophrenia %>%
  group_by(country) %>%
  summarise(mean_schizophrenia = mean(val))

# Log transform the mean_bulimia_nervosa column
schizophrenia$mean_schizophrenia <- log(schizophrenia$mean_schizophrenia)

# Scale the mean_bulimia_nervosa column (if needed)
schizophrenia$mean_schizophrenia <- scale(schizophrenia$mean_schizophrenia)

# Print the resulting data frame
print(schizophrenia)

```

```{r}
#######Conduct disorders#########
conduct_disorders <- gbd_mentalhealth %>%
  filter(cause == "Conduct disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(conduct_disorders$val)

# Optionally, calculate mean by country
conduct_disorders <- conduct_disorders %>%
  group_by(country) %>%
  summarise(mean_conduct_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
conduct_disorders$mean_conduct_disorders <- log(conduct_disorders$mean_conduct_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
conduct_disorders$mean_conduct_disorders <- scale(conduct_disorders$mean_conduct_disorders)

# Print the resulting data frame
print(conduct_disorders)


```

```{r}
########Eating disorders#########
eating_disorders <- gbd_mentalhealth %>%
  filter(cause == "Eating disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(eating_disorders$val)

# Optionally, calculate mean by country
eating_disorders <- eating_disorders %>%
  group_by(country) %>%
  summarise(mean_eating_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
eating_disorders$mean_eating_disorders <- log(eating_disorders$mean_eating_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
eating_disorders$mean_eating_disorders <- scale(eating_disorders$mean_eating_disorders)

# Print the resulting data frame
print(eating_disorders)

```
```{r}
########Bipolar disorder#########
bipolar_disorder <- gbd_mentalhealth %>%
  filter(cause == "Bipolar disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(bipolar_disorder$val)

# Optionally, calculate mean by country
bipolar_disorder <- bipolar_disorder %>%
  group_by(country) %>%
  summarise(mean_bipolar_disorder = mean(val))

# Log transform the mean_bulimia_nervosa column
bipolar_disorder$mean_bipolar_disorder <- log(bipolar_disorder$mean_bipolar_disorder)

# Scale the mean_bulimia_nervosa column (if needed)
bipolar_disorder$mean_bipolar_disorder <- scale(bipolar_disorder$mean_bipolar_disorder)

# Print the resulting data frame
print(bipolar_disorder)

```
```{r}
print(cause_table)
```
```{r}
########Substance use disorders########

substance_use_disorders <- gbd_mentalhealth %>%
  filter(cause == "Substance use disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(substance_use_disorders$val)

# Optionally, calculate mean by country
substance_use_disorders <- substance_use_disorders %>%
  group_by(country) %>%
  summarise(mean_substance_use_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
substance_use_disorders$mean_substance_use_disorders <- log(substance_use_disorders$mean_substance_use_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
substance_use_disorders$mean_substance_use_disorders <- scale(substance_use_disorders$mean_substance_use_disorders)

# Print the resulting data frame
print(substance_use_disorders)

```
```{r}
####Drug use disorders

drug_use_disorders <- gbd_mentalhealth %>%
  filter(cause == "Drug use disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(drug_use_disorders$val)

# Optionally, calculate mean by country
drug_use_disorders <- drug_use_disorders %>%
  group_by(country) %>%
  summarise(mean_drug_use_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
drug_use_disorders$mean_drug_use_disorders <- log(drug_use_disorders$mean_drug_use_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
drug_use_disorders$mean_drug_use_disorders <- scale(drug_use_disorders$mean_drug_use_disorders)

# Print the resulting data frame
print(drug_use_disorders)


```
```{r}
##########Alcohol use disorders
alcohol_use_disorders <- gbd_mentalhealth %>%
  filter(cause == "Alcohol use disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(alcohol_use_disorders$val)

# Optionally, calculate mean by country
alcohol_use_disorders <- alcohol_use_disorders %>%
  group_by(country) %>%
  summarise(mean_alcohol_use_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
alcohol_use_disorders$mean_alcohol_use_disorders <- log(alcohol_use_disorders$mean_alcohol_use_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
alcohol_use_disorders$mean_alcohol_use_disorders <- scale(alcohol_use_disorders$mean_alcohol_use_disorders)

# Print the resulting data frame
print(alcohol_use_disorders)

```
```{r}
#######Major depressive disorders
major_depressive_disorder <- gbd_mentalhealth %>%
  filter(cause == "Major depressive disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(major_depressive_disorder$val)

# Optionally, calculate mean by country
major_depressive_disorder <- major_depressive_disorder %>%
  group_by(country) %>%
  summarise(mean_major_depressive_disorder = mean(val))

# Log transform the mean_bulimia_nervosa column
major_depressive_disorder$mean_major_depressive_disorder <- log(major_depressive_disorder$mean_major_depressive_disorder)

# Scale the mean_bulimia_nervosa column (if needed)
major_depressive_disorder$mean_major_depressive_disorder <- scale(major_depressive_disorder$mean_major_depressive_disorder)

# Print the resulting data frame
print(major_depressive_disorder)

```
```{r}
#######Dysthymia########

dysthymia <- gbd_mentalhealth %>%
  filter(cause == "Dysthymia")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(dysthymia$val)

# Optionally, calculate mean by country
dysthymia <- dysthymia %>%
  group_by(country) %>%
  summarise(mean_dysthymia = mean(val))

# Log transform the mean_bulimia_nervosa column
dysthymia$mean_dysthymia <- log(dysthymia$mean_dysthymia)

# Scale the mean_bulimia_nervosa column (if needed)
dysthymia$mean_dysthymia <- scale(dysthymia$mean_dysthymia)

# Print the resulting data frame
print(dysthymia)


```
```{r}
library(dplyr)

# Perform left joins for each data frame on 'country'
final_mental_health_index <- mental_health_index %>%
  left_join(bulimia_nervosa, by = "country") %>%
  left_join(ADHD, by = "country") %>%
  left_join(Idiopathic_developmental_intellectual_disability, by = "country") %>%
  left_join(anorexia_nervosa, by = "country") %>%
  left_join(depressive_disorders, by = "country") %>%
  left_join(autismus_spectrum_disorders, by = "country") %>%
  left_join(conduct_disorders, by = "country") %>%
  left_join(schizophrenia, by = "country") %>%
  left_join(eating_disorders, by = "country") %>%
  left_join(bipolar_disorder, by = "country") %>%
  left_join(drug_use_disorders, by = "country") %>%
  left_join(alcohol_use_disorders, by = "country") %>%
  left_join(substance_use_disorders, by = "country") %>%
  left_join(major_depressive_disorder, by = "country") %>%
  left_join(dysthymia, by = "country")

# Display the resulting data frame
print(final_mental_health_index)

# Show the first few rows of the resulting data frame
head(final_mental_health_index)

```
```{r}
indicators <- left_join(final_mental_health_index, hardship, by = "country")
indicators
head(indicators)

new_data <- left_join (WVS_data, indicators, by = "country")
new_data


# Transfrom risk item such that high values represent more risk taking
new_data$risktaking = 6 - new_data$risktaking + 1

  
# Transform risk variable into T-score (mean = 50, sd = 10)
new_data$T_score_risktaking = 10*scale(new_data$risktaking, center=TRUE,scale=TRUE)+50

new_data

#Transform risk variable into Z score 

# Assuming T-scores have a mean of 50 and a standard deviation of 10
new_data$Z_score_risktaking = (new_data$T_score_risktaking - 50) / 10

# Print the resulting data frame
print(new_data)

new_data <- new_data %>%
  group_by(country) %>%
  mutate(z_score_age = scale(age))
new_data
```
```{r}
library(lme4)

library(lme4)

model <- lmer(T_score_risktaking ~ scale(z_score_age) * mental_health +
               gender * mental_health +
               factor(married) + factor(children) +
               factor(education) + factor(employed) +
               (1 + scale(z_score_age) + factor(married) + factor(children) + 
                factor(education) + factor(employed) | country),
             data = new_data)


summary(model)

# Assuming 'model' is a linear mixed-effects model (lmer), and you want to save coefficients to a CSV file

# Extract coefficients from the model summary
coefficients_df <- data.frame(summary(model)$coefficients)

# Write coefficients to a CSV file
write.csv(coefficients_df, "model_coefficients.csv", row.names = TRUE)

```
